1. Field of the Invention
The invention relates to correlating statistical records. More particularly, the invention relates to correlating compensation records to unique individual profiles.
2. Background of the Invention
Today, many reports are available that allow a user to find, read, purchase, or otherwise acquire reports on worker compensation. Most often these reports indicate average pay rates by industry, job type, locale, and sometimes they report more specific information about a particular industry or job, such as bonuses, stock options, average workweek, or immigration status, among other things. To create such compensation reports two approaches are typically used. One such approach is for a human analyst to research and find a statistically valid number of individuals with like characteristics, and devise a suite of compensation reports. This process is tedious, labor intensive, and often expensive. For a truly detailed report the analyst must be relied on to do substantial investigation and synthesize and apply this information to the case at hand. Compensation consultants with years of experience and resources can generally accurately profile an individual's worth in the market place, however such an analysis is extremely specialized and out of the reach of the typical consumer. Simpler and less costly reports are available but they are generally broadly classed and offer little utility.
The majority of software-based analysis provides a less expensive alternative but yields correspondingly limited information. Compensation services using current computer analysis programs generally gather data using some form of questionnaire and then feed the appropriate data into a computer database or spreadsheet. More typically, generalized data, such as from the US Bureau of Labor and Statistics, is used as a base and then extrapolated based on region and date, and often combined with third party surveys. Typically, a computer then is instructed to run an analysis of the data to provide statistical information, such as averages, medians, and standard deviations on pre-determined groups of people. However the information provided is not unique to an individual, but instead, is a conglomeration of data that the program determines best represents the individual. Because the categorization of the individual is based solely on a limited, predetermined set of responses to the questionnaire, it offers little to no opportunity for evaluating unique characteristics. For example, an automated compensation service may categorize and calculate data showing that the average yearly salary of a “Computer Programmer Level 3” in Washington state is $64,250. This may or may not be applicable to a “Senior Application Software Engineer” with “10 years of experience” and special training in the skill “C++,” but because the closest answer describing the Senior Application Software Engineer's position in the initial survey was a “Computer Programmer Level 3,” the Senior Application Software Engineer has thus been categorized ineffectively, which removes any unique abilities that he may possess.
The Senior Application Software Engineer reading the aforementioned report cannot be sure how closely the published report figures apply to himself individually. There are a multitude of factors that affect any one individual's job compensation. The current generalized reporting methods for compensation reports, cannot and do not incorporate factors that provide for an accurate job comparison and compensation analysis for individual users. Today's methods require the user to gauge or self-approximate themselves to a group of people being reported. Typically, such approximations are grouped by a specific job title that a human compensation analyst predetermines when creating a report, or when designing a computer service that eventually generates the report. This grouping is generally not an exact match with the user's actual job title and responsibilities and often has little applicability to the users individual qualities. For example, the compensation analyst might have created a report for an isolated group called Computer Programmer Level 3. For individuals who possess the same characteristics as the data files used to create this group, the reports generated from such a compensation analysis are reasonably accurate. However, for individuals possessing unique capabilities, experiences, skills, or talents, the reports are essentially useless. The data are by definition misapplied because any differences in the compared data are arbitrarily reflected in the compensation report. This introduces doubt on the user's part as to how closely he can trust the report's applicability.
Current compensation analysis techniques do not provide users with affordable, accurate, and personalized compensation reports. Job specific variables, critical to the accurate assessment of an individual's worth, are not correctly identified or uniformly applied. Furthermore individuals within a particular field are unaware of the value of certain, often easily obtainable, qualifications. There is a need, therefore, for an apparatus and method that provides online compensation reports using a more flexible survey system to produce dynamic profiles based on unique individual attributes, and to provide automated comparisons and reports that account for these attributes.